Winning lessons from the early innings of data science in commercial real estate

It's only been a few years since CRE firms began adopting data science initiatives. Part of the reason may be that other industries have experienced data science projects as a long ballgame. Yet there are notable efforts that outscore the competition.

To learn more about how data science innovators in the commercial real estate industry are winning big for their teams so quickly in the ballgame, we spoke with four successful early adopters.

They candidly shared the challenges they face, the strategies they deploy, and the outcomes they are experiencing. Their winning lessons can be invaluable in helping you consider the best data science approaches to yield the advantages that will attract customers and add value to your business.

A personal journey to scale in data science:

“My work has pivoted from generating reports to creating an agile engine to handle a wider range of use cases. Here we have the scope and platform to drive more data science advancements.”

Mark Franceski

Avison Young

• The organization: Avison Young, global commercial real estate advisor.

• The lead: Mark Franceski, Executive Director and global AVANT leader for residential and capital market sectors.

• The data science driver: Prior to joining Avison Young in 2021, Franceski was VP Research for The Bozzuto Group, a large East Coast multifamily management, development and construction firm. As data science was emerging, he realized growth now required a stronger level of analysis.

• The challenges: “This required big shifts to upgrade the types of data inputs used, upgrade how analysis would be presented and shared, and a lot in between.” Franceski also had to elevate his own skill base by “learning how to deal with dimensional modeling, Power BI, inexpensive low-code options platforms, and deploying platforms like StratoDem Analytics to squeeze out deeper insights faster.”

• The strategies: AVANT is Avison Young’s predictive analytics system to generate stronger market insights for the firm’s clients. “There’s an advantage created in integrating data to pick up trends, enable better mapping and visualizations, and make higher-level analysis understandable for clients.”

• The outcome: “Data science has created a first-mover advantage over our larger competitors, and scored some early wins so clients are betting that we’ll continue our progress towards real advancements in predictive analytics.”

• Winning lessons: Starting from a small team, AVANT now has more than 150 employees around the world. Getting to the forefront of a major data science initiative globally in part reflects Franceski’s continuing effort to build his personal data science knowledge as fast as the data science field advances.

Finding solutions that put data in front of teams when they need it:

“We aren’t tech professionals. We just happen to be industry professionals who are one step ahead on the tech side.”

Andrew Weakland

W.P. Carey

• The organization: W.P. Carey, one of the largest net-lease REITs, with 1,400 properties across North America and Europe.

• The lead: Andrew Weakland, Senior Vice President and Director of Systems Development.

• The data science driver: Weakland is expected to generate insights that can help the entire team beat the market. So, the motivation for targeted deployment of data science is simple: “We need to move analysis beyond traditional modelling to improve decisions at the times when decisions are getting made.”

• The challenges:

  • Weakland is not building a large in-house structure for data science.

  • “I would never try to hire a top data scientist internally. Our core focus is real estate investing, and that talent is also rare. But for a top-tier data scientist, you’re also competing against the FAANGs (Facebook, Amazon, Apple, Netflix, Google), and I simply won’t compete in dollars for top engineering talent. By the time you build out that team, the cash committed for that team would be better deployed into growing the real estate team. Or even deployed directly into real estate.”

• The strategies:

  • “We’re looking for solutions to move from constant requests for reports to putting the data in front of the team when they need it, instead of in the hands of data scientists.” To harness best-of-breed applications in the market,

  • “The amount of venture capital that’s flowed into proptech is helping the market mature faster. And the best vendors are making those tools more digestible for the commercial real estate market.”

  • “But it’s more than just deploying tools. It’s about making sure they resonate with people who’ve spent their entire careers in real estate.”

• The outcome: “These tools help our work get better when we find points that match the intuition of the professionals on our team and tell stories that match their innate feel of the market.”

• Winning lessons:

  • “In the end, we make highly complex binary decisions. Should I buy or sell? We have to consider lots of variables and factors, and we need to optimize the human intelligence of highly niche talent by arming them with better tools.”

  • "Machines aren’t going to be making those binary decisions for the humans on our team. But machines are getting better at helping us finding anomalies, which our teams can then exploit.”

Creating the optimal environment to optimize data science:

“We still work with the brokerage community, still drive through neighborhoods, still talk with tenants, still examine buildings. We still do all the things that other real estate investors do. But we’re willing to make the bet on data science advancements. Uncovering emerging trends and pricing discrepancies is doable. We’ve seen enough to know this will be a differentiator”

Adam Brueckner

Silverstone Partners

• The organization: Silverstone Partners, real estate investment manager for the multifamily sector in high-growth US markets.

• The lead: Adam Brueckner, Managing Principal.

• The data science driver :

  • Silverstone Partners is focused on “generating superior returns for tomorrow by innovating real estate investment today.”

  • The company’s strategy is grounded in the belief that “data should play a significant role in our investment process, including identifying the best opportunities, understanding competitive dynamics, and combining these insights with local knowledge and best-in-class execution.”

• The challenges:

  • Having observed the commercial real estate world for 13 years, Brueckner understands what it’s like at both established real estate investment funds with significant resources to deploy data science, and at startup funds without this advantage.

  • “Within the real estate industry as a whole, there is generally less familiarity with data science as it is simply at a very early stage of exploration. When presented with a machine learning black-box model and someone says, ‘trust us,’ this is understandably a very difficult bridge to cross for seasoned, established professionals who possess several decades of experience. Until machine learning becomes explainable, it makes sense that senior leadership is skeptical.”

  • Also, “delivering strong data science outcomes takes time. Even 18 months would be pretty good, but that can invite a downward spiral for funding when they can’t generate good results fast enough.”

  • Another headwind is cultural. “At more established funds, there’s a base of smart professionals who really understand real estate, but who built their expertise before data analytics became part of the picture. And they bring on new hires who understand analytics, but not real estate. The result can be like oil and water.”

• The strategies:

  • “The bottom line is that it’s hard to turn a real estate company into an analytics-driven company overnight.” This realization led Brueckner to launch Silverstone Partners, a smaller real estate investment fund driven by advanced analytics at the core.

  • This involves conducting traditional analysis and advanced analysis side-by-side, and building a team where everyone has a high level of comfort working with advanced analytics.

• The outcome:

  • When fundamental analysis finds that between 50%-80% of excess returns over a five-year period are driven by market selection, it forces rethinking the role of analysis.

  • “Deploying data science and elementary machine learning becomes a valuable tool for building a strong market-selection framework. It’s fundamentally about understanding what matters most and what doesn’t matter as much.”

• Winning lessons:

  • “In real estate, 95% of the industry still invests with a traditional mindset. This gap is going to create a big advantage for firms willing to leverage both the traditional process and a more advanced data-driven evaluation process…at least until most of the field adapts to this shift.”

  • The future is clear: “The winning combination is going to be putting together the smart blend of human + machine."

    
    

Ongoing discussions with stakeholders to sharpen the product fit:

“Rather than building out overly complicated infrastructure, we focus on the specific analytics that acquisitions teams need to move forward or pass on a deal. That matters because there are firms that work at immense scale over a huge number of markets, and there are firms that are extremely knowledgeable in a few home markets. We’re carving out our own space by competing with a unique set of insights that others don’t have.”

Gabe Greenberg

Tishman Speyer

• The organization: Tishman Speyer, a leading owner, developer, operator and investment manager of top-tier real estate in 33 key markets across the US, Europe, Asia, and Latin America.

• The lead: Gabe Greenberg, Senior Director, Head of Data Science & Analytics.

• The data science driver:

  • “Most interesting to us is the idea that in other industries, there’s a huge number of data sources. But in real estate, there’s not really that much data, and everyone has access to most of the same sources.”

  • This realization led Greenberg’s team to head down the path of connecting extensive data across every possible tax parcel.

• The challenges:

  • “We looked deeply across markets, built dashboards and tools that analyzed millions of properties across markets, and the acquisitions team thought it was interesting. But they didn’t use it. Those weren’t the tools they wanted to address the questions they needed to answer.”

• The strategies:

  • Unlike at some other firms where data science efforts fizzle if the early results are inconclusive, Greenberg continued discussions with stakeholders and sharpened the internal product fit.

  • “We figured out what work is repetitive. There’s better adaption when we take the boring pieces and get those done more effectively. We aim for the questions that are repeatedly asked, then build the tools that make it easier to pull out the actionable insights.”

• The outcome:

  • Now, his focus includes enabling the rapid scanning and scoring of the firm’s relevant markets, as well as tracking changes in underlying submarkets.

  • “By quantifying pieces of the underwriting process and systematically identifying submarket characteristics for stronger investment results, the qualitative human overlay can kick in faster, acquisitions teams can present better deals, and our investment committee can make more informed decisions.”

• Winning lessons: “Teams that disappear on their own to work on “the next best thing” often find that their customers have moved on to other problems by the time they’re done. Instead of working in a data science silo, we’ve had the most success by partnering directly with our internal clients to answer the problems they face daily, and automating the tedious but trivial parts of their work.”

Data science progress requires a symphony of software and teams

Given the high hurdles to get data science initiatives in motion, moving forward requires more than aspiration. It requires strong need and motivation to commit to a sometimes painful or costly process. For many early adopters, the most significant learnings tend to be about change management and the human dimension.

As the head of strategic partnerships and business development at MetaProp, one of the prominent early-stage venture capital funds in the real estate arena, Zander Geronimos has a unique vantage point for observing the motivations and challenges for real estate data science.

“As data science initiatives are creating better data… the biggest shifts ultimately require change management across the organizational and capital stack. I have started to see what I dub as a symphony of software and investment management teams, where investment teams across asset types start to cooperate from a data perspective on the demographic and psychographic trends that drive users to their sites.”

Adds Geronimos, “It is only a recent phenomenon which will continue to grow as folks become more comfortable sharing information through their platforms.”

While we’re only in the early innings of utilizing data science in commercial real estate, the lessons shared by these innovators can add context as you strategize the best approaches to win your ballgame.